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Record W3215472297 · doi:10.1111/irfi.12370

<scp>COVID</scp>‐19 and hedge fund equity ownership

2021· article· en· W3215472297 on OpenAlex
Laleh Samarbakhsh, Amanjot Singh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Review of Finance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsWestern UniversityThe King's UniversityToronto Metropolitan University
Fundersnot available
KeywordsHedge fundBusinessEquity (law)Leverage (statistics)Global assets under managementAlternative betaCoronavirus disease 2019 (COVID-19)Fund of fundsProfitability indexMonetary economicsFinancial systemFinanceInstitutional investorEconomicsCorporate governanceMarket liquidityInternal medicine

Abstract

fetched live from OpenAlex

Abstract This study investigates hedge funds equity ownership in light of the COVID‐19 pandemic. Using the merged dataset of Lipper TASS hedge funds and the corresponding 13F filings, we find that with the start of the pandemic, hedge funds increased their equity ownership toward firms with less financial constraints, such as larger firms, firms with lower leverage, and more profitability. Moreover, hedge funds increased their ownership in firms which had higher overall risk (political and non‐political), and lower overall sentiment. Hedge funds also care about firms' exposure/sensitivity toward different political issues such as health care, technology &amp; infrastructure, and security &amp; defense. This suggests that hedge funds seek equity ownership in riskier stocks as a result of pandemic uncertainties.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.139
GPT teacher head0.359
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it